Hyperspectral remote sensing images acquired by imaging a ground object by a remote sensor in visible, near-infrared, mid-infrared, and thermal infrared wavelength ranges of the electromagnetic spectrum not only provide spatial and geometrical information of the ground object, but also contain abundant spectral information that represents physical properties specific to the ground object. Therefore, hyperspectral classification techniques have come into being for identifying a ground object by extracting features and knowledge, such as the spectrum, texture, and shape, of the ground object, from a hyperspectral remote sensing image.
The earliest hyperspectral classification technique simply used spectral features for classification. However, due to the influence of various factors such as illumination, climate changes, cloud thickness, and mixed image elements, the phenomena of same object with different spectrums and same spectrum for different objects often occur in a hyperspectral remote sensing image, which leads to severe misclassification. Also, since a hyperspectral remote sensing image contains abundant spatial structure information about the ground object, precision in ground object classification can be improved effectively by taking overall consideration of the spatial and spectral information in hyperspectral data, thereby obtaining a classification map with desirable spatial continuity. Research on hyperspectral remote sensing image classification that combines spectral features with spatial features has become a current hot topic, and the essential issue for such research consists in extracting spatial structure information such as the texture, shape, object, and semantics, and combining appropriately the spectral information and the spatial features. Depending on different combinations of spectral features and spatial features, spectral-spatial classification techniques can be roughly classified into two strategies: combined processing and fused processing.
With a combined processing strategy, the extracted spatial features and spectral features are both input to a classifier to obtain a classification result (briefly referred to as spatial information preprocessing), or otherwise, object regularization is performed on the original classification result by means of image segmentation, so as to obtain a classification map with high spatial homogeneity (briefly referred to as spatial information postprocessing). Spatial information preprocessing mainly involves spatial feature extraction processes such as morphological analysis and spatial filtering based on edge preserving and sparse representation, and spatial information postprocessing mainly involves processes such as multiple logistic regression and hypergraph generation. Due to introduction of spatial information, the combined spectral-spatial feature classification process is effective in classification and low in calculation complexity. However, the hyperspectral remote sensing image itself has a three-dimensional structure, and yet the spatial features and spectral features obtained through the combined spatial information processing are separated, therefore the contextual relationship between the spectrum and the spatial structure has been ignored. Meanwhile, the spatial information postprocessing relies on the classification result, therefore misclassification of most of one type of ground objects may be exacerbated by postprocessing.
With a fused processing strategy, an integrated spatial-spectral feature description is obtained by performing mathematical operations directly on the original spectral data by using a set of predefined multiscale kernels or three-dimensional filters. As in such a process, the three-dimensional hyperspectral remote sensing image is processed as a whole, such that the contextual relationship between the spectral domain and the spatial domain can be adequately mined, this process has received increasing attention. However, since the spectral and spatial distribution structure of the ground object is generally unknown, a sufficient number of scales or three-dimensional filters has to be defined in order to obtain a sufficient amount of integrated spatial-spectral representative features, resulting in an extremely large feature dimension and a large feature redundance, which makes the classification process very time-consuming, thereby reducing practicability of the algorithm.
Currently, the contextual relationship between the spectral domain and the spatial domain has been ignored in prior art, and therefore, depiction and extraction of the intrinsic structural and statistical relationship between spectral and spatial information in the data are not adequate or accurate and may generate a large amount of redundant information.